Using linked Census ancestry data to examine all-cause mortality by ethnicity in Australia.

Area of Science:

• Public Health
• Epidemiology
• Sociology

Background:

• Routine collection of ethnicity data for Australia's non-Indigenous population is lacking in health records.
• The Australian Census collects proxy ancestry data, offering a potential avenue for health inequality research.
• Existing methods using country of birth or standard ancestry data may obscure ethnic health disparities.

Purpose of the Study:

• To develop and apply an improved approach for using ancestry data to examine ethnicity-based health inequalities in Australia's non-Indigenous population.
• To specifically investigate all-cause mortality inequalities among ethnic groups, particularly Māori and Pasifika populations.
• To compare the effectiveness of the novel approach against traditional methods in detecting mortality disparities.

Main Methods:

• An expert and community panel informed the categorization of ancestry data, prioritizing ethnic minority identities and refining continental categories.
• The study linked 2016 Australian Census data (20.3 million people) with death registrations from 2016-2021.
• Mortality inequalities were analyzed using the developed approach, comparing results with standard country of birth and unrefined ancestry methods.

Main Results:

• The novel ancestry approach revealed significantly higher mortality in Māori females and Melanesian, Papuan, and Polynesian males and females compared to country of birth data.
• For example, Māori females showed mortality rates of 747 vs 507 per 100,000 person-years when using the refined ancestry method versus country of birth.
• The expert and community-informed approach identified larger mortality inequalities than existing methods, highlighting the impact of refined data categorization.

Conclusions:

• The developed approach effectively utilizes Australian Census ancestry data to enhance the identification of mortality inequalities in Māori and Pasifika ethnic groups.
• Standard methods relying on country of birth or unrefined ancestry data can mask or underestimate critical health disparities.
• This research underscores the importance of nuanced data interpretation for accurate public health surveillance and targeted interventions.